Regular monitoring of common physiological signs, including heart rate, blood pressure, and oxygen saturation, can be an effective way to either prevent or detect many kinds of chronic conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 32% of all deaths worldwide are from CVDs. In addition, stress-related issues cost $190 billion in healthcare costs per year. Currently, contact devices are required to extract most of an individual’s physiological information, which can be uncomfortable for users and can cause discomfort. However, in recent years, remote photoplethysmography (rPPG) technology is gaining growing interest, which enables contactless monitoring of the blood volume pulse signal using a regular camera, and ultimately can provide the same physiological information as a contact device. In this paper, we propose a benchmark comparison using a new multimodal database consisting of 56 subjects where each subject was submitted to three different tasks. Each subject wore a wearable device capable of extracting photoplethysmography signals and was filmed to allow simultaneous rPPG signal extraction. Several experiments were conducted, including a comparison between information from contact and remote signals and stress state recognition. Results have shown that in this dataset, rPPG signals were capable of dealing with motion artifacts better than contact PPG sensors and overall had better quality if compared to the signals from the contact sensor. Moreover, the statistical analysis of the variance method had shown that at least two HRV features, NNi 20 and SAMPEN were capable of differentiating between Stress and Non-Stress states. In addition, three features, IBI, NNi 20, and SAMPEN were capable of differentiating between tasks relating to different levels of difficulty. Furthermore, using machine learning to classify a ‘stressed’ or ‘unstressed’ state, the models were able to achieve an accuracy score of 83.11%.

中文翻译：

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评估视觉光电容积描记法

定期监测常见生理体征，包括心率、血压和氧饱和度，是预防或检测多种慢性病的有效方法。尤其是，心血管疾病 (CVD) 是全球关注的问题。根据世界卫生组织的数据，全世界 32% 的死亡来自心血管疾病。此外，与压力相关的问题每年花费 1900 亿美元的医疗保健费用。目前，需要接触设备来提取个人的大部分生理信息，这会让用户感到不舒服并且会引起不适。然而，近年来，远程光电容积脉搏波 (rPPG) 技术越来越受到关注，它可以使用普通相机对血容量脉冲信号进行非接触式监测，并最终可以提供与接触设备相同的生理信息。在本文中，我们提出了使用由 56 个主题组成的新多模式数据库的基准比较，其中每个主题都被提交给三个不同的任务。每个受试者都佩戴一个能够提取光体积描记信号的可穿戴设备，并被拍摄以允许同时提取 rPPG 信号。进行了几项实验，包括比较来自接触和远程信号的信息以及压力状态识别。结果表明，在该数据集中，rPPG 信号能够比接触式 PPG 传感器更好地处理运动伪影，并且与来自接触式传感器的信号相比，总体质量更好。此外，方差方法的统计分析表明，至少有两个 HRV 特征，NNi 20 和 SAMPEN 能够区分压力和非压力状态。此外，IBI、NNi 20 和 SAMPEN 三个功能能够区分与不同难度级别相关的任务。此外，使用机器学习对“压力”或“无压力”状态进行分类，模型能够达到 83.11% 的准确率。